DOCSLIB.ORG

Seismic Data Filtering Using Non-Local Means Algorithm Based on Structure Tensor

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Seismic Data Filtering Using Non-Local Means Algorithm Based on Structure Tensor Open Geosci. 2017; 9:151–160 Research Article Open Access Shuai Yang, Anqing Chen*, and Hongde Chen Seismic data filtering using non-local means algorithm based on structure tensor DOI 10.1515/geo-2017-0013 Received Sep 22, 2016; accepted Feb 03, 2017 1 Introduction Abstract: Non-Local means algorithm is a new and ef- Filtering is a key step in seismic data processing [1]. Due to fective filtering method. It calculates weights of all sim- increasing demands for high-quality data, interpretation- ilar neighborhoods’ center points relative to filtering oriented processing becomes more and more important. point within searching range by Gaussian weighted Eu- Improved signal-to-noise ratio (S/N) by filtering process- clidean distance between neighborhoods, then gets fil- ing may facilitate subsequent studies of seismic geomor- tering point’s value by weighted average to complete phology, seismic facies and micro-structures as well as the filtering operation. In this paper, geometric distance reservoir prediction. of neighborhood’s center point is taken into account in Seismic data filtering may be realized through Gaus- the distance measure calculation, making the non-local sian filtering, median filtering, mean filtering and vari- means algorithm more reasonable. Furthermore, in order ous transform-domain approaches, e.g. Fourier transform- to better protect the geometry structure information of based f -k and f-x filtering, wavelet transform-based filter- seismic data, we introduce structure tensor that can de- ing [2, 3], curvelet transform-based filtering [4] and Radon pict the local geometrical features of seismic data. The co- transform-based filtering. Additionally, there is the filter- herence measure, which reflects image local contrast,is ing technique based on a geostatistical method which extracted from the structure tensor, is integrated into the makes use of the variogram function after decomposition non-local means algorithm to participate in the weight to do geostatistical estimation respectively to get the es- calculation, the control factor of geometry structure sim- timated value of the various components of the original ilarity is added to form a non-local means filtering algo- data, including the effective information and noise values. rithm based on structure tensor. The experimental results It has been applied to filtering of seismic data [5]. These prove that the algorithm can effectively restrain noise, with techniques are effective for seismic data filtering, but most strong anti-noise and amplitude preservation effect, im- of them have the disadvantages of blurring the details proving PSNR and protecting structure information of seis- and edges of an image in the process of noise reduction. mic image. The method has been successfully applied in Marginal reflections or seismic discontinuities may be seismic data processing, indicating that it is a new and ef- caused by faults, fractures, channels, lenticular geobodies fective technique to conduct the structure-preserved filter- or reefs. Such features are closely related to the interpre- ing of seismic data. tation of hydrocarbon reservoirs. Consequently some new techniques, e.g. filtering based on mathematical morphol- Keywords: Non-Local means; Structure tensor; Filter; Sim- ogy [6], structure constrained edge-preserved filtering [7], ilarity; Interpretative processing and anisotropic diffusion filtering based on partial differ- ential equation [8], have been developed to preserve edge structures or the contacts between reflections and forma- Shuai Yang: College of Geosciences, China University of tions. Petroleum(Beijing), Beijing, 102249, China; Institute of Sedimen- Buades et al. made a comparative study of some typi- tary Geology, Chengdu University of Technology, Chengdu, 610059, cal filtering techniques and developed a non-local means China; Email: [email protected] (NLM) algorithm for noise removal [9–11]. This algorithm *Corresponding Author: Anqing Chen: Institute of Sedimen- tary Geology, Chengdu University of Technology, Chengdu, 610059, was demonstrated to have better performance than other China; Email: [email protected]; Tel.: +86-028-84073252; typical denoising methods. David Bonar et al. success- Fax: +86-028-84078992 fully applied the NLM algorithm in seismic data denoising Hongde Chen: Institute of Sedimentary Geology, Chengdu with the idea of neighborhood matching and correlation University of Technology, Chengdu, 610059, China; Email: as well as weighted smoothing [12, 13], but they did not [email protected] © 2017 S. Yang et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. 152 Ë S. Yang et al. take account of seismic geometric properties such as di- and its neighborhood Ni to the pixel j and its neighborhood rectivity and local contrast. Apparently, if a seismic pro- Nj. Note that each pixel i has its own independent weight- file or attribute slice is considered as a digital image, each ing factor of the other pixels j within the image, which is pixel would be characterized by two properties, i.e. numer- calculated by Equation (3). ical property of the pixel itself and geometric structure of (︂ )︂ 1 −D2(i, j) neighboring pixels. In this paper, we develop a new filter- w(i, j) = exp (3) Z(i) h2 ing algorithm for seismic data (image) processing. It inte- P grates similarity evaluation by local contrast with NLM fil- where Z(i) is the normalizing factor to ensure w(i, j) = 1, tering based on structure tensor, which captures the geo- j and defined by metric structure of an image [8]. The calculation of neigh- (︂ )︂ borhood similarity distance involves numerical similar- X −D2(i, j) Z(i) = exp (4) h2 ity and neighborhood geometric distance. A coherence at- j tribute, designed to describe local contrast, is added into weighting factor estimation to evaluate similarity. A con- The filtering parameter h is a constant which controls trol factor is also included to adjust the weight of geomet- the decay rate of the exponential function and thus deter- ric property. As per model data processing and residual er- mines the degree of filtering. For example, a large value ror images, this algorithm achieves a better result of edge for h will provide very similar weight for all pixels j, thus preservation. In addition, a large control factor reduces the the image would be blurred. On the contrary, a small value weight of geometric property for better noise attenuation. for h will provide a significant weight for only a few ofthe The results of field data processing also show that this al- pixels j, thus noises cannot be attenuated sufficiently. 2 gorithm is capable of edge preservation while noise sup- The Gaussian weighted Euclidean distance D (i, j) in pression. Equation (3) is defined by the following expression. D2 i j ⃒v N v N ⃒2 ( , ) = ⃒ ( i) − ( j)⃒2,a (5) nl X [︀ ]︀2 2 Theoretical studies = Ga(xl , yl) (v(Ni(l)) − v(Nj(l))) l 2.1 NLM filtering j j2 where the operator • 2, a denotes the squared factor of 2 Gaussian weighted Euclidean distance D (i, j), Ni repre- Previous denoising algorithms were developed under the sents a neighborhood with the center at the pixel i, which assumption that the raw image is regular. Moreover many is usually a square domain, Ga represents the Gaussian details of the image may also be smoothed in denoising. kernel with the standard deviation a, and l represents one These two disadvantages have been remedied by NLM fil- of the total nl elements within a neighborhood. tering developed by Buades et al. [14]. The basic idea of For a 2D image, the Gaussian kernel can be defined by, this algorithm is that an image usually contains a mass of (︂ 2 2 )︂ noises, thus an image v is defined as (x − x0) + (y − y0) Ga(x, y) = exp − (6) 2a2 v = u + n (1) where X0 and y0 represent the center of Gaussian kernel As per Equation (1), the image v is composed of the with x and y corresponding to the coordinates of the ele- original noise-free image u and random noise n. At the ment l in equation (5). pixel i, the NLM filtering result bv(i) is simply the weighted For three neighborhoods (in red, green and blue, re- average of all of pixels within the noisy image v. spectively) around a square image (in Figure 1a), the X weights of the centers in respect to other all of elements ^v(i) = w(i, j)v(j) (2) within the image are calculated by Equation (3). We ob- j viously can see, for red neighborhood (in Figure 1a), the In Equation (2), the weighting factor w(i, j) is dependent centers of all red neighborhoods in Figure 1b have larger on the similarity between pixels i and j, in addition must weighting factors due to good similarity. Similarly for P satisfy the conditions 0 ≤ w(i, j) ≤ 1 and w(i, j) = 1. The green neighborhood (in Figure 1a), the centers of all green j neighborhoods in Figure 1c have larger weighting factors. similarity between the pixels i and j is represented by Gaus- For blue neighborhood (in Figure 1a), the centers of all blue sian weighted Euclidean distance D(i, j) from the pixel i neighborhoods in Figure 1d have larger weighting factors. Seismic data filtering using non-local means algorithm Ë 153 center may be somewhat mitigated. In numerical calcula- tion, w(i, i)=0.5 or w(i, i) = max(w(i, j)8i≠j). In following calculation, w(i, i) = 0.5. Each pixel in the image would be compared with all pixels in the process of NLM filtering, which results in mass computation and low efficiency. For an image with M×N pixels, M×N weighting factors would be calculated repeat- edly for each pixel.
Load more

Recommended publications
  • Machine Generated Curvilinear Structures Extracted from Magnetic Data in the Fort Mcmurray Oil Sand Area Using 2D Structure Tensor Hassan H
    Machine generated curvilinear structures extracted from magnetic data in the Fort McMurray oil sand area using 2D structure tensor Hassan H. Hassan Multiphysics Imaging Technology Summary Curvilinear structures are among the most important geological features that are detected by geophysical data, because they are often linked to subsurface geological structures such as faults, fractures, lithological contacts, and to some extent paleo-channels. Thus, accurate detection and extraction of curvilinear structures from geophysical data is crucial for oil and gas, as well as mineral exploration. There is a wide-range of curvilinear structures present in geophysical images, either as straight lines or curved lines, and across which image magnitude changes rapidly. In magnetic images, curvilinear structures are usually associated with geological features that exhibit significant magnetic susceptibility contrasts. Traditionally, curvilinear structures are manually detected and extracted from magnetic data by skilled professionals, using derivative or gradient based filters. However, the traditional approach is tedious, slow, labor-intensive, subjective, and most important they do not perform well with big geophysical data. Recent advances in digital imaging and data analytic techniques in the fields of artificial intelligence, machine and deep learnings, helped to develop new methods to automatically enhance, detect, and extract curvilinear structures from images of small and big data. Among these newly developed techniques, we adopted one that is based on 2D Hessian structure tensor. Structure tensor is a relatively modern imaging technique, and it reveals length and orientation of curvilinear structures along the two principal directions of the image. It is based on the image eigenvalues (λ1, λ2) and their corresponding eigenvectors (e1, e2), respectively.
    [Show full text]
  • Edge Orientation Using Contour Stencils Pascal Getreuer
    Edge Orientation Using Contour Stencils Pascal Getreuer To cite this version: Pascal Getreuer. Edge Orientation Using Contour Stencils. SAMPTA’09, May 2009, Marseille, France. Special session on sampling and (in)painting. hal-00452291 HAL Id: hal-00452291 https://hal.archives-ouvertes.fr/hal-00452291 Submitted on 1 Feb 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Edge Orientation Using Contour Stencils Pascal Getreuer (1) (1) Department of Mathematics, University of California Los Angeles [email protected] Abstract: structure tensor J( u) = u u. The struc- ture tensor satisfies ∇J( u)∇ = J⊗( ∇u) and u is an eigenvector of J( u).−∇ The structure∇ tensor takes∇ into Many image processing applications require estimat- ∇ ing the orientation of the image edges. This estimation account the orientation but not the sign of the direc- is often done with a finite difference approximation tion, thus solving the antipodal cancellation problem. of the orthogonal gradient. As an alternative, we ap- As developed by Weickert [9], let ply contour stencils, a method for detecting contours from total variation along curves, and show it more Jρ( uσ) = Gρ J(Gσ u) (1) ∇ ∗ ∗ robustly estimates the edge orientations than several where Gσ and Gρ are Gaussians with standard devia- finite difference approximations.
    [Show full text]
  • Plane-Wave Sobel Attribute for Discontinuity Enhancement in Seismic Imagesa
    Plane-wave Sobel attribute for discontinuity enhancement in seismic imagesa aPublished in Geophysics, 82, WB63-WB69, (2017) Mason Phillips and Sergey Fomel ABSTRACT Discontinuity enhancement attributes are commonly used to facilitate the interpre- tation process by enhancing edges in seismic images and providing a quantitative measure of the significance of discontinuous features. These attributes require careful pre-processing to maintain geologic features and suppress acquisition and processing artifacts which may be artificially detected as a geologic edge. We propose the plane-wave Sobel attribute, a modification of the classic Sobel filter, by orienting the filter along seismic structures using plane-wave destruction and plane- wave shaping. The plane-wave Sobel attribute can be applied directly to a seismic image to efficiently and effectively enhance discontinuous features, or to a coherence image to create a sharper and more detailed image. Two field benchmark data examples with many faults and channel features from offshore New Zealand and offshore Nova Scotia demonstrate the effectiveness of this method compared to conventional coherence attributes. The results are reproducible using the Madagascar software package. INTRODUCTION One of the major challenges of interpreting seismic images is the delineation of reflection discontinuities that are related to distinct geologic features, such as faults, channels, salt boundaries, and unconformities. Visually prominent reflection features often overshadow these subtle discontinuous features which are critical to understanding the structural and depositional environment of the subsurface. For this reason, precise manual interpretation of these reflection discontinuities in seismic images can be tedious and time-consuming, es- pecially when data quality is poor. Discontinuity enhancement attributes are among the most widely used seismic attributes today.
    [Show full text]
  • 1 Introduction
    1: Introduction Ahmad Humayun 1 Introduction z A pixel is not a square (or rectangular). It is simply a point sample. There are cases where the contributions to a pixel can be modeled, in a low order way, by a little square, but not ever the pixel itself. The sampling theorem tells us that we can reconstruct a continuous entity from such a discrete entity using an appropriate reconstruction filter - for example a truncated Gaussian. z Cameras approximated by pinhole cameras. When pinhole too big - many directions averaged to blur the image. When pinhole too small - diffraction effects blur the image. z A lens system is there in a camera to correct for the many optical aberrations - they are basically aberrations when light from one point of an object does not converge into a single point after passing through the lens system. Optical aberrations fall into 2 categories: monochromatic (caused by the geometry of the lens and occur both when light is reflected and when it is refracted - like pincushion etc.); and chromatic (Chromatic aberrations are caused by dispersion, the variation of a lens's refractive index with wavelength). Lenses are essentially help remove the shortfalls of a pinhole camera i.e. how to admit more light and increase the resolution of the imaging system at the same time. With a simple lens, much more light can be bought into sharp focus. Different problems with lens systems: 1. Spherical aberration - A perfect lens focuses all incoming rays to a point on the optic axis. A real lens with spherical surfaces suffers from spherical aberration: it focuses rays more tightly if they enter it far from the optic axis than if they enter closer to the axis.
    [Show full text]
  • Detection and Evaluation Methods for Local Image and Video Features Julian Stottinger¨
    Technical Report CVL-TR-4 Detection and Evaluation Methods for Local Image and Video Features Julian Stottinger¨ Computer Vision Lab Institute of Computer Aided Automation Vienna University of Technology 2. Marz¨ 2011 Abstract In computer vision, local image descriptors computed in areas around salient interest points are the state-of-the-art in visual matching. This technical report aims at finding more stable and more informative interest points in the domain of images and videos. The research interest is the development of relevant evaluation methods for visual mat- ching approaches. The contribution of this work lies on one hand in the introduction of new features to the computer vision community. On the other hand, there is a strong demand for valid evaluation methods and approaches gaining new insights for general recognition tasks. This work presents research in the detection of local features both in the spatial (“2D” or image) domain as well for spatio-temporal (“3D” or video) features. For state-of-the-art classification the extraction of discriminative interest points has an impact on the final classification performance. It is crucial to find which interest points are of use in a specific task. One question is for example whether it is possible to reduce the number of interest points extracted while still obtaining state-of-the-art image retrieval or object recognition results. This would gain a significant reduction in processing time and would possibly allow for new applications e.g. in the domain of mobile computing. Therefore, the work investigates different corner detection approaches and evaluates their repeatability under varying alterations.
    [Show full text]
  • Structure-Oriented Gaussian Filter for Seismic Detail Preserving Smoothing
    STRUCTURE-ORIENTED GAUSSIAN FILTER FOR SEISMIC DETAIL PRESERVING SMOOTHING Wei Wang*, Jinghuai Gao*, Kang Li**, Ke Ma*, Xia Zhang* *School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China. **China Satellite Maritime Tracking and Control Department, Jiangyin, 214431, China. [email protected] ABSTRACT location. Among the different methods to achieve the denoising of 3D seismic data, a large number of approaches This paper presents a structure-oriented Gaussian (SOG) using nonlinear diffusion techniques have been proposed in filter for reducing noise in 3D reflection seismic data while the recent years ([6],[7],[8],[9],[10]). These techniques are preserving relevant details such as structural and based on the use of Partial Differential Equations (PDE). stratigraphic discontinuities and lateral heterogeneity. The Among these diffusion based seismic denoising Gaussian kernel is anisotropically constructed based on two methods, an excellent sample is the seismic fault preserving confidence measures, both of which take into account the diffusion (SFPD) proposed by Lavialle et al. [10]. The regularity of the local seismic structures. So that, the filter SFPD filter is driven by a diffusion tensor: shape is well adjusted according to different local wU divD JUV U U (1) geological features. Then, the anisotropic Gaussian is wt steered by local orientations of the geological features Anisotropy in the smoothing processes is addressed in terms (layers) provided by the Gradient Structure Tensor. The of eigenvalues and eigenvectors of the diffusion tensor. potential of our approach is presented through a Therein, the diffusion tensor is based on the analysis of the comparative experiment with seismic fault preserving Gradient Structure Tensor ([4],[11]): diffusion (SFPD) filter on synthetic blocks and an §·2 §·wwwwwUUUUUVVVVV application to real 3D seismic data.
    [Show full text]
  • Coding Images with Local Features
    Int J Comput Vis (2011) 94:154–174 DOI 10.1007/s11263-010-0340-z Coding Images with Local Features Timo Dickscheid · Falko Schindler · Wolfgang Förstner Received: 21 September 2009 / Accepted: 8 April 2010 / Published online: 27 April 2010 © Springer Science+Business Media, LLC 2010 Abstract We develop a qualitative measure for the com- The results of our empirical investigations reflect the theo- pleteness and complementarity of sets of local features in retical concepts of the detectors. terms of covering relevant image information. The idea is to interpret feature detection and description as image coding, Keywords Local features · Complementarity · Information and relate it to classical coding schemes like JPEG. Given theory · Coding · Keypoint detectors · Local entropy an image, we derive a feature density from a set of local fea- tures, and measure its distance to an entropy density com- puted from the power spectrum of local image patches over 1 Introduction scale. Our measure is meant to be complementary to ex- Local image features play a crucial role in many computer isting ones: After task usefulness of a set of detectors has vision applications. The basic idea is to represent the image been determined regarding robustness and sparseness of the content by small, possibly overlapping, independent parts. features, the scheme can be used for comparing their com- By identifying such parts in different images of the same pleteness and assessing effects of combining multiple de- scene or object, reliable statements about image geome- tectors. The approach has several advantages over a simple try and scene content are possible. Local feature extraction comparison of image coverage: It favors response on struc- comprises two steps: (1) Detection of stable local image tured image parts, penalizes features in purely homogeneous patches at salient positions in the image, and (2) description areas, and accounts for features appearing at the same lo- of these patches.
    [Show full text]
  • Lecture Foils
    INF 5300 - 8.4.2015 Detecting good features for tracking Anne Schistad Solberg . Finding the correspondence between two images . What are good features to match? . Points? . Edges? . Lines? INF 5300 1 Curriculum .Chapter 4 in Szeliski, with a focus on 4.1 Point-based features .Recommended additional reading on SIFT features: . Distinctive Image Features from Scale-Invariant Keypoints by D. Lowe, International Journal of Computer Vision, 20,2,pp.91-110, 2004. 2 Goal of this lecture • Consider two images containing partly the the same objects but at different times or from different views. • What type of features are best for recognizing similar object parts in different images? • Features should work on different scales and rotations. • These features will later be used to find the match between the images. • This chapter is also linked to chapter 6 which we will cover in a later lecture. • This is useful for e.g. – Tracking an object in time – Mosaicking or stitching images – Constructing 3D models 3 Image matching • How do we compute the correspondence between these images? – Extract good features for matching (this lecture) – Estimating geometrical transforms for matching (later lecture) •by Diva Sian •by swashford What type of features are good? •Point-like features? •Region-based features? •Edge-based features? •Line-based features? 5 Point-based features • Point-based features should represent a set of special locations in an image, e.g. landmarks or keypoints. • Two main categories of methods: – Find points in an image that can be easily tracked, e.g. using correlation or least-squares matching. • Given one feature, track this feature in a local area in the next frame • Most useful when the motion is small – Find features in all images and match them based on local appearance.
    [Show full text]
  • A Spatialized Model of Textures Perception Using Structure Tensor Formalism Grégory Faye, Pascal Chossat
    A spatialized model of textures perception using structure tensor formalism Grégory Faye, Pascal Chossat To cite this version: Grégory Faye, Pascal Chossat. A spatialized model of textures perception using structure tensor formalism. Networks and Heterogeneous Media, AIMS-American Institute of Mathematical Sciences, 2013, pp.fay-cho2013. hal-00807371 HAL Id: hal-00807371 https://hal.archives-ouvertes.fr/hal-00807371 Submitted on 3 Apr 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Manuscript submitted to Website: http://AIMsciences.org AIMS’ Journals Volume X, Number 0X, XX 200X pp. X–XX A SPATIALIZED MODEL OF VISUAL TEXTURE PERCEPTION USING THE STRUCTURE TENSOR FORMALISM Gregory´ Faye School of Mathematics, University of Minnesota 206 Church Street S.E., Minneapolis, MN 55455, USA and Neuromathcomp Project Team, INRIA Sophia Antipolis 2004 Route des Lucioles, F-06902 Sophia Antipolis Pascal Chossat J-A Dieudonn´eLaboratory, CNRS and University of Nice Sophia-Antipolis Parc Valrose, 06108 Nice Cedex 02, France and Neuromathcomp Project Team, INRIA Sophia Antipolis (Communicated by the associate editor name) Abstract. The primary visual cortex (V1) can be partitioned into funda- mental domains or hypercolumns consisting of one set of orientation columns arranged around a singularity or “pinwheel” in the orientation preference map.
    [Show full text]
  • Structure Tensor Based Analysis of Cells and Nuclei Organization in Tissues
    Structure tensor based analysis of cells and nuclei organization in tissues Wenxing Zhang, Jérôme Fehrenbach, Annaick Desmaison, Valérie Lobjois, Bernard Ducommun, Pierre Weiss To cite this version: Wenxing Zhang, Jérôme Fehrenbach, Annaick Desmaison, Valérie Lobjois, Bernard Ducommun, et al.. Structure tensor based analysis of cells and nuclei organization in tissues. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2016, 10.1109/TMI.2015.2470093. hal-01037972 HAL Id: hal-01037972 https://hal.archives-ouvertes.fr/hal-01037972 Submitted on 23 Jul 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Structure tensor based analysis of cells and nuclei organization in tissues Wenxing Zhang1,2,3, Jérôme Fehrenbach1,2,4,5, Annaïck Desmaison1,2, Valérie Lobjois1,2, Bernard Ducommun1,2,6, and Pierre Weiss ∗1,2,4,5 1CNRS, ITAV-USR3505, Toulouse, France. 2Université de Toulouse, ITAV-USR3505, Toulouse, France. 3School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China. 4CNRS, IMT-UMR5219, Toulouse, France. 5Université de Toulouse, IMT-UMR5219, Toulouse, France. 6CHU de Toulouse, Toulouse, France. July 23, 2014 Abstract Motivation: Extracting geometrical information from large 2D or 3D biomedical images is important to better understand fundamental phe- nomena such as morphogenesis.
    [Show full text]
  • Context-Aware Features and Robust Image Representations
    Accepted Manuscript Context-Aware Features and Robust Image Representations P. Martins, P. Carvalho, C. Gatta PII: S1047-3203(13)00186-7 DOI: http://dx.doi.org/10.1016/j.jvcir.2013.10.006 Reference: YJVCI 1279 To appear in: J. Vis. Commun. Image R. Received Date: 7 March 2013 Accepted Date: 26 October 2013 Please cite this article as: P. Martins, P. Carvalho, C. Gatta, Context-Aware Features and Robust Image Representations, J. Vis. Commun. Image R. (2013), doi: http://dx.doi.org/10.1016/j.jvcir.2013.10.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Context-Aware Features and Robust Image Representations P. Martinsa,∗, P. Carvalhoa, C. Gattab aCenter for Informatics and Systems, University of Coimbra, Coimbra, Portugal bComputer Vision Center, Autonomous University of Barcelona, Barcelona, Spain Abstract Local image features are often used to efficiently represent image content. The limited number of types of features that a local feature extractor responds to might be insufficient to provide a robust image repre- sentation. To overcome this limitation, we propose a context-aware feature extraction formulated under an information theoretic framework. The algorithm does not respond to a specific type of features; the idea is to retrieve complementary features which are relevant within the image context.
    [Show full text]
  • Structure Tensor Estimation of Local Orientation • a Very Simple Description of Local Orientation at Image Point P = (U,V) Is Given By
    TSBB15 Computer Vision Lecture 3 The structure tensor Estimation of local orientation • A very simple description of local orientation at image point p = (u,v) is given by: • Here, ∇I is the gradient at point p of the image intensity I. In practice: 2 Estimation of local orientation • Problem 1: ∇I may be zero, even though there is a well defined orientation. • Problem 2: The sign of ∇I changes across a line. 3 Estimation of local orientation • Partial solution: • Form the outer product of the gradient with itself: ∇I ∇TI. • This is a symmetric 2 × 2 matrix (tensor) • Problem 2 solved! • Also: The representation is unambiguous • Distinct orientations are mapped to distinct matrices • Similar orientations are mapped to similar matrices • Continuity / compatibility • Problem 1 remains 4 The structure tensor • Compute a local average of the outer product of the gradients around the point p: • Here, x represent an offset from p • w2(x) is some LP-filter (typically a Gaussian) • T is a symmetric 2 × 2 matrix: Tij = Tji • This construction is called the structure tensor • Solves also problem 1 (why?) • T is computed for each point p in the image 5 Orientation representation • For a signal that is approximately i1D in the neighborhood of a point p, with orientation ±n: ∇I is always parallel to n (why?) • The gradients that are estimated around p are a scalar multiple of n • The average of their outer products results in T = λnnT • for some value λ • λ depends on w1, w2, and the local signal I 6 Motivation for T • The structure tensor has been derived based on several independent approaches For example • Stereo tracking (Lucas & Kanade, 1981) (Lec.
    [Show full text]